Table REF  shows the quantitative comparisons on the DVD dataset. As shown, our model performs favorably against previous works in both metrics, indicating a better restoration quality.
In Fig. REF  we show the deblurred images output from different models.
The work [1]} designs an end-to-end convolution models for deblurring video frames. However, they rely mainly on explicit alignment methods such as homography or optical flows only, therefore are less favorable to restore abrupt motion blurs.
The method [2]} uses only two consecutive frames as the contextual information, thus do not fully utilize the temporal information in the video. As can be seen in Figure REF , it fails to fully restore the structure of scene objects.
We further note that the method [3]} computes a temporal sharpness prior to guide the model to focus on areas with severe blurs. However, the prior only considers a small range of pixels, thereby, it is less effective when fast motions are present.
In contrast, our model takes in multiple consecutive frames as input, providing stronger video contextual clues.
By implicitly constructing the pixel-pair correspondence across all the spatial range, our model is able to effectively captures pixel correlations with large displacements.
This is particularly effective for blurs due to fast motions.
As a result, our proposed model restores better the detailed scene content.
